5,844 research outputs found
Generative agents for player decision modeling in games
This paper presents a method for modeling player decision
making through the use of agents as AI-driven personas.
The paper argues that artificial agents, as generative player
models, have properties that allow them to be used as psychometrically valid, abstract simulations of a human player’s
internal decision making processes. Such agents can then be
used to interpret human decision making, as personas and
playtesting tools in the game design process, as baselines for
adapting agents to mimic classes of human players, or as believable, human-like opponents. This argument is explored
in a crowdsourced decision making experiment, in which the
decisions of human players are recorded in a small-scale dungeon themed puzzle game. Human decisions are compared
to the decisions of a number of a priori defined “archetypical”
agent-personas, and the humans are characterized by their
likeness to or divergence from these. Essentially, at each
step the action of the human is compared to what actions
a number of reinforcement-learned agents would have taken
in the same situation, where each agent is trained using a
different reward scheme. Finally, extensions are outlined for
adapting the agents to represent sub-classes found in the
human decision making traces.peer-reviewe
Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data
We consider learning, from strictly behavioral data, the structure and
parameters of linear influence games (LIGs), a class of parametric graphical
games introduced by Irfan and Ortiz (2014). LIGs facilitate causal strategic
inference (CSI): Making inferences from causal interventions on stable behavior
in strategic settings. Applications include the identification of the most
influential individuals in large (social) networks. Such tasks can also support
policy-making analysis. Motivated by the computational work on LIGs, we cast
the learning problem as maximum-likelihood estimation (MLE) of a generative
model defined by pure-strategy Nash equilibria (PSNE). Our simple formulation
uncovers the fundamental interplay between goodness-of-fit and model
complexity: good models capture equilibrium behavior within the data while
controlling the true number of equilibria, including those unobserved. We
provide a generalization bound establishing the sample complexity for MLE in
our framework. We propose several algorithms including convex loss minimization
(CLM) and sigmoidal approximations. We prove that the number of exact PSNE in
LIGs is small, with high probability; thus, CLM is sound. We illustrate our
approach on synthetic data and real-world U.S. congressional voting records. We
briefly discuss our learning framework's generality and potential applicability
to general graphical games.Comment: Journal of Machine Learning Research. (accepted, pending
publication.) Last conference version: submitted March 30, 2012 to UAI 2012.
First conference version: entitled, Learning Influence Games, initially
submitted on June 1, 2010 to NIPS 201
Modeling Human Ad Hoc Coordination
Whether in groups of humans or groups of computer agents, collaboration is
most effective between individuals who have the ability to coordinate on a
joint strategy for collective action. However, in general a rational actor will
only intend to coordinate if that actor believes the other group members have
the same intention. This circular dependence makes rational coordination
difficult in uncertain environments if communication between actors is
unreliable and no prior agreements have been made. An important normative
question with regard to coordination in these ad hoc settings is therefore how
one can come to believe that other actors will coordinate, and with regard to
systems involving humans, an important empirical question is how humans arrive
at these expectations. We introduce an exact algorithm for computing the
infinitely recursive hierarchy of graded beliefs required for rational
coordination in uncertain environments, and we introduce a novel mechanism for
multiagent coordination that uses it. Our algorithm is valid in any environment
with a finite state space, and extensions to certain countably infinite state
spaces are likely possible. We test our mechanism for multiagent coordination
as a model for human decisions in a simple coordination game using existing
experimental data. We then explore via simulations whether modeling humans in
this way may improve human-agent collaboration.Comment: AAAI 201
The Dreaming Variational Autoencoder for Reinforcement Learning Environments
Reinforcement learning has shown great potential in generalizing over raw
sensory data using only a single neural network for value optimization. There
are several challenges in the current state-of-the-art reinforcement learning
algorithms that prevent them from converging towards the global optima. It is
likely that the solution to these problems lies in short- and long-term
planning, exploration and memory management for reinforcement learning
algorithms. Games are often used to benchmark reinforcement learning algorithms
as they provide a flexible, reproducible, and easy to control environment.
Regardless, few games feature a state-space where results in exploration,
memory, and planning are easily perceived. This paper presents The Dreaming
Variational Autoencoder (DVAE), a neural network based generative modeling
architecture for exploration in environments with sparse feedback. We further
present Deep Maze, a novel and flexible maze engine that challenges DVAE in
partial and fully-observable state-spaces, long-horizon tasks, and
deterministic and stochastic problems. We show initial findings and encourage
further work in reinforcement learning driven by generative exploration.Comment: Best Student Paper Award, Proceedings of the 38th SGAI International
Conference on Artificial Intelligence, Cambridge, UK, 2018, Artificial
Intelligence XXXV, 201
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
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